Utility companies generate electrical power, often at many hundred thousand volt levels, and distribute the power over a reconfigurable network grid system to various customer loads. Conservative system design requires anticipation of the many contingent modes of failure that may occur in such a system. For example, if a generator fails to output sufficient power, the defective generator must be taken off-line to remove the outage fault condition. Next, the grid is reconfigured so customers formerly supplied electricity from the failed generator can receive power from another generator. Of course many other faults can also contribute to a system failure.
Any of these failure modes, and others as well, may lead to fault conditions that can damage the distribution system, perhaps catastrophically, and/or cause great inconvenience to the customer. In each instance, once the defective system components are identified and taken off-line, a post-fault system equilibrium condition is attained and analyzed. A decision is then made to reconfigure the grid in a given fashion to continue to supply electricity, to the best of available resources.
But once post-fault equilibrium condition is reached, the grid may be reconfigured in a great number of ways, some of which will be more optimum than others. Further, determining the best reconfiguration of the remaining system may have to be accomplished within a matter of minutes. The problem then is how to best determine an optimum system reconfiguration in a finite amount of time.
It is known in the art to employ on-line dynamic security analysis ("DSA") of a power system, the goal being to provide the power system operator with timely information on transfer limits and system stability margins. However, attaining this information requires analysis of hundreds of transient and dynamic stability contingencies approximately every ten minutes using time domain simulations. The large number of contingencies that must be considered is dictated by reliability requirements needed to ensure that all potentially severe, e.g., unstable, contingencies are considered.
Typically, prior art dynamic security analysis techniques assume that predicting system security with respect to a given contingency, requires a knowledge of (a) the post-fault equilibrium condition, (b) severity of the disturbance as given by system state deviation system immediately after fault-clearing from post-fault equilibrium, and (c) system ability to withstand the disturbance as given by the maximum potential energy at the relevant unstable equilibrium point of the post-fault system.
In pre-selecting contingencies for further processing, the prior art relies upon experience from previous studies including past experience from actual faults in the power system, and judgment of the engineers operating the power system. As such, pre-selection is based upon an expected or desired state of the power system. However, such desired state may be unrelated to the actual state of the system for which new studies are to be conducted.
To reduce the insecurity inherent in not knowing whether the desired state and the actual system state are the same, an unnecessarily large set of potential outages is created that is assumed valid for a wide range of operating conditions. Further, separate lists of outages may be created for each specifically-identified pattern of system operations.
Selecting the appropriate contingencies out of potentially hundreds of contingencies is, however, itself an extremely computationally intensive and time consuming process. Further, simulating the more relevant-appearing contingencies is itself a computationally demanding process. It is also recognized that even a small subset of the considered outage contingencies may present dynamic security analysis problems. In short, either too many outages are considered, or there is a risk that an important outage may go unconsidered. This uncertainty is compounded by the fact that there is no assurance that pre-selection will be proper for the actual conditions experienced by the power system.
However they were selected, the more relevant appearing contingencies are then simulated on a computer system using algorithmic procedures to approximate their stability effects upon the power system. Relevant contingencies are those that appear to produce an unsafe and unstable state of the power system following the fault. By contrast, less relevant contingencies appear to result in a safe and stable power system state following the fault.
It is important to appreciate the speed requirements for simulation according to the present invention. Realistically a power system may include a great many power generators and perhaps 2,500 busses. It is important that no potential contingencies be excluded from the analysis, and thus as many as 500 contingencies must be accounted for. The number of contingencies to be considered is also large as there is no prior knowledge about the potential severity of many outages.
Notwithstanding the large number of contingencies to be considered, in practice total simulation should occur in less than an hour, and preferably in about 30 minutes, with perhaps a 20 second stability simulation requirement. But even using state of the art processing equipment, computational time is excessive. For example, evaluating 500 contingencies using a Digital Equipment Corporation ALPHA 150 MIP chip would take about 12 hours.
Simply stated, prior art techniques for on-line dynamic security analysis are simply not feasible because the computation requirements result in a turnaround time of many hours, even using the fastest commercial computer hardware economically practical.
What is needed is a more rapid method for selecting the more relevant contingencies to be analyzed in the event of a power system fault. Such method should avoid time domain analysis, and complete a meaningful analysis of the fault presented within a matter of minutes.
The present invention discloses such a method for winnowing down the set of potential contingencies to be analyzed.